Hire an AI Meta Ads results reviewer

Everything it can hand back

Your task sets the actual outputs. The specialist picks the methods that cover them.

• A setup check that says if your dates, currency, attribution and metrics line up
• A ranked findings table with the evidence, the sum and how sure the AI is for each row
• A tracking check that lists what is confirmed and what is not
• A list of evidence gaps and the checks that did not run
• A proposed actions list, with nothing changed in your account
• A checkpoint file for your next review

How it does the job well

• Use only the account and dates the user named.
• Do not invent numbers, results, approvals or a completed action. Keep proposed settings apart from observed values.
• Thresholds and platform labels you know from memory are starting assumptions, not current rules. Say "check this in Ads Manager" when you rely on one.
• Never put secrets, tokens or logins in a file or chat.
• A budget change, pause, new ad or client message stays a proposal unless the user approved that exact action in this chat.
• Clarify the purpose, audience and limits, such as length, format and deadline, first.
• Use the user's materials and voice. Ask for samples when tone matters.

Mistakes it avoids

• Blaming an ad for a result that tracking or low volume could explain.
• Mixing periods with different attribution settings.
• Summing platform conversions from different channels as if they were unique customers.
• Calling this continuous monitoring. It is one review at a time.
• Generic output that ignores the user's details.
• Inventing facts, quotes or sources.

What a good result looks like

An example, not your task: Your Meta results moved and you need to know if the cause is the ads, the tracking or too little data.

• setup-check.md. Whether dates, time zone, currency, attribution and metric definitions match, with every mismatch listed.
• findings.md. Ranked findings, each with evidence ID, the sum behind it, confidence and a proposed action.
• tracking-check.md. Each tracking part marked Confirmed, Not confirmed or Not supplied, with the evidence for it.
• evidence-gaps.md. What is missing, which finding it limits and how to get it.
• proposed-actions.md. Each change you could make, with where, why, risk and a note that nothing was done.
• checkpoint.md. Scope, evidence dates, open questions, approvals and the trigger for the next review.

How the work is checked

• The output meets the stated length and format.
• Every fact comes from the user's material or is marked to check.
• Suggested changes explain why.
• States which checks passed, failed or could not run, and names missing inputs.

These checks have not been run against a model yet. Your AI uses them to check its own work.

Capabilities

• Fix the scope. It confirms the account, dates, time zone, currency, attribution setting and your goal. Anything missing is asked once, then marked MISSING.
• Load the last checkpoint. If you supply one, it compares scope and dates and says what is new. Your current request wins over the old checkpoint.
• Check the numbers line up. It recomputes cost per result and return on ad spend from the raw columns and compares them with the exported ones.
• Check tracking. It reads your tracking evidence and marks each part Confirmed, Not confirmed or Not supplied.
• Read the results. It reviews campaigns, ad sets and ads against your targets, your own earlier numbers, creative mix and change history.
• Rank and propose. Each finding gets an evidence ID, the sum behind it and a confidence level. Actions stay proposals until you approve that exact action.
• Save a checkpoint. It records scope, evidence dates, open questions and the next review trigger, so a later run can compare.

How the role works

1. Checks your task, context and available tools.
2. Picks the methods needed and prepares a work plan.
3. Carries out supported tasks, with each method's limits and review gates.
4. Checks the real deliverables and corrects supported faults.
5. Hands back files, evidence and unresolved gaps.

Your specialist's working setup

Download the base specialist without a task form or account. README.md and SETUP.md explain how to use the folder. SOUL.md defines its scope. INTAKE.md asks missing questions inside your AI. PLAYBOOK.md, SKILLS.md, BEST_PRACTICES.md, CHECKS.md and HANDOFF.md guide the work and its checked deliverables.

Complete associated skills, supporting files and licences are included. Choose only the methods needed for the actual task. Keep context, working files and accepted feedback in your AI project.

Use this specialist through MCP or the API

Call prepare_specialist with role_id meta-results-review-agent only. No task is required to load it. Your AI asks what job you need done, what you want back and which relevant context it can use.

For task-led hiring, call find_specialist, explain the suggested specialists and let the user accept or change the team.

Complete ZIP: Download base specialist. API: GET /api/v1/specialists/meta-results-review-agent/pack. The host AI supplies its own model, tools and execution; a pack does not connect accounts or run a task.

Inputs and helpful context

The specialist starts from the task brief. Only active methods require their inputs.

For the primary method, when used

• Ads Manager exports for one date range, at campaign, ad set and ad level, with spend, results, conversion value and frequency
• Your account time zone, currency and attribution setting, for example 7-day click
• Your targets, such as the cost per sale or return on ad spend you need
• Proof that tracking works, such as Events Manager screenshots of your events and match quality (optional)
• Your last checkpoint file (optional)
• Change history for the same dates (optional)

Helpful context (optional)

• Your break-even return on ad spend, worked out from your margin.
• Your store or CRM sales totals for the same dates.
• The previous period exports, with the same attribution setting.

Tools and access

• Claude, ChatGPT or Gemini with file upload: The agent reads several exports and screenshots at once and does sums on them.

Functions and deliverables

• Meta Ads results reviewer: Review your Meta Ads results against your targets. Get ranked findings, tracking gaps, proposed fixes and a checkpoint for the next review.
  • A setup check that says if your dates, currency, attribution and metrics line up
  • A ranked findings table with the evidence, the sum and how sure the AI is for each row
  • A tracking check that lists what is confirmed and what is not
  • A list of evidence gaps and the checks that did not run
  • A proposed actions list, with nothing changed in your account
  • A checkpoint file for your next review
• Meta test campaign planner: Share ad concepts, target cost per sale and budget. Get test groups, naming rules, settings checks and bounded spend plans.
  • A campaign and ad set layout sized to your store
  • A pack plan with ad names, minimum spend and removal date
  • A CBO or ABO call with the trade-off written out
  • A settings checklist, naming guide and rules for adding ads and scaling
• Meta pixel and server events fixer: Share tracking setup and event evidence. Audit browser and server events, plan fixes and verify them with authorised tools.
  • A tracking audit
  • A match quality score
  • A list of missing events and data
  • Fixes for your platform
  • A test plan to confirm they work
  Source: Matt Berman.
• Conversion tracking setup and repair planner: Define a sale or lead. Get a plan to set up or repair event tracking across your named ad platforms.
  • Setup or repair steps
  • Event and tag list
  • Audit verdict per platform
  • Verification checklist
  Source: Corey Haines.
• me.md builder: Answer a few questions, or let your AI use what it remembers. Get one private file that tells any AI who you are.
  • One me.md file with up to ten dated sections
  • A list of facts taken from memory, for you to check
  • A short note of what it removed for privacy
  • The two next steps, to save it and set it up

Tasks you can use it for

• Your Meta results moved and you need to know if the cause is the ads, the tracking or too little data.
• A client or boss asks for a Meta Ads review that shows its evidence.
• You want to check results against a target cost per sale or return on ad spend.
• You review every month and want each review to start from the last one.

Limits to check before starting

• You want a score across 30 account checks. Use the Meta Ads 30-check account auditor.
• You want the same checks every Monday and a scale-or-hold call. Use the Meta Ads weekly account reviewer.
• You have one odd symptom, such as ads that will not spend. Use the Meta delivery troubleshooter.

Best way to use this role

Give it one clear outcome, the actual files and a definition of done. Keep the work in one AI project so it can reuse checked context and earlier files.

• Download the pack from this page and add it to your AI. If you cannot add it, paste SKILL.md into a new chat. Claude, ChatGPT and Gemini all work if they can read files.
• Export your results from Ads Manager for one clear date range and attach the files. No Meta login is needed.
• Type your time zone, currency, attribution setting and targets. Attach your tracking screenshots and last checkpoint if you have them.
• Say "Run the review. Propose changes, do not make any." Your AI asks once for anything missing.
• Open setup-check.md first. If the numbers do not line up, fix the exports before you trust the findings.
• Check each finding against its evidence ID. Make the changes you agree with in Ads Manager yourself, then keep checkpoint.md for next time.

Read it as data

Read the complete role setup as JSON. Connect your AI to the AI Blocks MCP library and read aiblocks://roles/meta-results-review-agent for its role instructions and pinned sources.

This is a prepared AI role. No task has run. Your AI carries out authorized work using its available tools and reports the actual deliverables and checks.